Description Usage Arguments Details Value Author(s) References See Also Examples

Estimates the average treatment effect (ATE) and inferential statistics under constant effects hypotheses. Estimation is without covariate adjustment, via weighted least squares.

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`Y` |
numeric vector of length N, outcome variable |

`Z` |
binary vector (0 or 1) of length N, treatment indicator |

`perms` |
N-by-r permutation matrix, as output by |

`invert` |
logical for generating constant effects confidence intervals through exact test inversion, with the difference-in-means as a test statistic. Default is |

`quantiles` |
vector of quantiles of the randomization distribution to be returned. Quantiles also used to determine endpoints of confidence intervals. Default is equal-tailed 95% intervals. |

`omni.ate()`

is a convenience function that implements a number of functions otherwise available in `ri`

. Greater flexibility through use of the individual functions involved.

`ate` |
estimated average treatment effect |

`greater.p.value` |
one-tailed p-value: proportion of randomizations yielding estimated ATE greater than or equal to hypothesized ATE |

`lesser.p.value` |
one-tailed p-value: proportion of randomizations yielding estimated ATE less than or equal to hypothesized ATE |

`p.value` |
two-tailed p-value: twice the smaller of the two one-tailed p-values, as advocated by Rosenbaum (2002) |

`p.value.alt` |
two-tailed p-value: proportion of randomizations yielding absolute estimated ATE greater than or equal to absolute hypothesized ATE |

`se.null` |
standard error of the randomization distribution assuming a zero treatment effect |

`conf.int` |
confidence interval approximation under a constant effect hypothesis |

`se` |
standard error of the randomization distribution assuming a constant treatment effect equal to the estimated ATE |

`conf.intInv` |
(Optional, if |

Peter M. Aronow <[email protected]>; Cyrus Samii <[email protected]>

Gerber, Alan S. and Donald P. Green. 2012. *Field Experiments: Design, Analysis, and Interpretation*. New York: W.W. Norton.

Rosenbaum, Paul R. 2002. *Observational Studies*. 2nd ed. New York: Springer.

Samii, Cyrus and Peter M. Aronow. 2012. On Equivalencies Between Design-Based and Regression-Based Variance Estimators for Randomized Experiments. *Statistics and Probability Letters*. 82(2): 365-370. http://dx.doi.org/10.1016/j.spl.2011.10.024

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